Cryptology ePrint Archive: Report 2021/755

Tetrad: Actively Secure 4PC for Secure Training and Inference

Nishat Koti and Arpita Patra and Rahul Rachuri and Ajith Suresh

Abstract: In this work, we design an efficient mixed-protocol framework, Tetrad, with applications to privacy-preserving machine learning. It is designed for the four-party setting with at most one active corruption and supports rings.

Our fair multiplication protocol requires communicating only $5$ ring elements improving over the state-of-the-art protocol of Trident (Chaudhari et al. NDSS'20). The technical highlights of Tetrad include efficient (a) truncation without any overhead, (b) multi-input multiplication protocols for arithmetic and boolean worlds, (c) garbled-world, tailor-made for the mixed-protocol framework, and (d) conversion mechanisms to switch between the computation styles. The fair framework is also extended to provide robustness without inflating the costs.

The competence of Tetrad is tested with benchmarks for deep neural networks such as LeNet and VGG16 and support vector machines. One variant of our framework aims at minimizing the execution time, while the other focuses on the monetary cost. We observe improvements up to $6\times$ over Trident across these parameters.

Category / Keywords: cryptographic protocols / 4PC, fair, robust, multi-party computation, privacy preserving machine learning

Date: received 5 Jun 2021, last revised 8 Jun 2021

Contact author: kotis at iisc ac in, arpita at iisc ac in, rachuri at cs au dk, ajith at iisc ac in

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Version: 20210608:074239 (All versions of this report)

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